Mr Lilun Du (University of Wisconsin Madison)
Single-Index Modulated Multiple Testing

Abstract: In the context of large-scale multiple testing, hypotheses are often accompanied with certain prior information. In this talk, we present a single-index modulated (SIM) multiple testing procedure, which maintains control of the false discovery rate while incorporating prior information, by assuming the availability of a bivariate p-value, $(p_1, p_2)$, for each hypothesis, where $p_1$ is a preliminary p-value from prior information and $p_2$ is the primary p-value for the ultimate analysis. To find the optimal rejection region for the bivariate p-value, we propose a criteria based on the ratio of probability density functions of $(p_1, p_2)$ under the true null and non-null. This criteria in the bivariate normal setting further motivates us to project the bivariate p-value to a single-index, $p(\theta)$, for a wide range of directions $\theta$. The true null distribution of $p(\theta)$ is estimated via parametric and nonparametric approaches, leading to two procedures for estimating and controlling the false discovery rate. To derive the optimal projection direction $\theta$, we propose a new approach based on power comparison, which is further shown to be consistent under some mild conditions. Simulation evaluations indicate that the SIM multiple testing procedure improves the detection power significantly while controlling the false discovery rate. Analysis of a real dataset will be illustrated.